Enhancing Deep Learning Models for Real-Time Decision Making in Autonomous Systems

Abstract

The rapid advancements in deep learning techniques have significantly impacted various fields, with autonomous systems being one of the most promising applications. Autonomous systems, ranging from self-driving vehicles to unmanned aerial vehicles (UAVs) and industrial robots, rely on real-time decision-making capabilities to function effectively in dynamic and uncertain environments. This paper investigates the enhancement of deep learning models for real-time decision-making in autonomous systems. Traditional deep learning models often face challenges in terms of latency, generalization, and the ability to adapt to rapidly changing scenarios in real-time. These limitations necessitate the development of more efficient, scalable, and robust deep learning architectures tailored for real-time decision-making. The research examines the integration of advanced techniques such as reinforcement learning, deep reinforcement learning (DRL), and neural architecture search (NAS) to optimize deep learning models for faster decision-making while ensuring high accuracy. Reinforcement learning, in particular, is explored for its capacity to enable autonomous systems to learn optimal policies through interaction with the environment, thus improving real-time decision-making in complex, real-world settings. Additionally, the paper discusses the importance of transfer learning to adapt pre-trained models to new environments and tasks, reducing the time required for training and enhancing model performance in dynamic environments. Key challenges addressed in this work include ensuring minimal computational overhead, optimizing the model to handle large-scale sensor data, and enhancing model robustness against unforeseen environmental factors. A hybrid architecture combining convolutional neural networks (CNNs) with recurrent neural networks (RNNs) and attention mechanisms is proposed to process both spatial and temporal data efficiently. The proposed model is evaluated across multiple autonomous system platforms, including robotics and autonomous vehicles, demonstrating improved decision-making speeds and overall system performance.Furthermore, this study emphasizes the necessity of a multi-agent approach for complex scenarios where multiple autonomous entities must interact and make coordinated decisions. The incorporation of multi-agent reinforcement learning (MARL) is explored to enable autonomous systems to collaborate and make joint decisions in real-time. Experiments show that the proposed models outperform conventional deep learning systems in terms of decision-making speed, accuracy, and adaptability in dynamic environments.